{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas(Index=0, num_legs=4, num_wings=0)\n",
      "Pandas(Index=1, num_legs=2, num_wings=2)\n",
      "Pandas(Index=0, num_legs=1, num_wings=0)\n",
      "Pandas(Index=1, num_legs=2, num_wings=2)\n"
     ]
    }
   ],
   "source": [
    "\"测试itertuples能否修改源数据\"\n",
    "import pandas as pd\n",
    "df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]})\n",
    "for item in df.itertuples():\n",
    "    print(item)\n",
    "df.at[0,'num_legs']=1\n",
    "for item in df.itertuples():\n",
    "    print(item)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6]]\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "# 将数组存储的时候dumps编码成字符串，读取的时候解码成数组\n",
    "import json\n",
    "import numpy as np\n",
    "a = np.array([[1,2,3],[2,3,4],[3,4,5],[4,5,6]])\n",
    "b = np.array([1,2,3,4,5,6])\n",
    "data = json.dumps(a.tolist())\n",
    "print(data)\n",
    "data = json.loads(data)\n",
    "print(type(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n",
      "3\n",
      "————————————————————————\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'numpy.int64'>\n",
      "<class 'str'>\n",
      "————————————————————————\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'numpy.int64'>\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "# 直接将numpy存储到csv中，读取会变成str，所以先使用json编码，读取的时候解码\n",
    "from obspy import read\n",
    "from obspy.core.utcdatetime import UTCDateTime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "st = read(r'G:\\data\\streams\\GSOK027_12-2014.mseed')\n",
    "print(len(st))\n",
    "start_time = st[0].stats.starttime\n",
    "event = st.slice(UTCDateTime(start_time),UTCDateTime(start_time)+10).copy()\n",
    "print(len(event))\n",
    "\n",
    "data = [tr.data[:1000] for tr in event]\n",
    "data = np.array(data, dtype=np.float32)\n",
    "data = json.dumps(data.tolist())\n",
    "\n",
    "channels = [tr.stats.channel for tr in event]\n",
    "station_ids = [tr.stats.station for tr in event]\n",
    "channels = json.dumps(channels)\n",
    "station_ids = json.dumps(station_ids)\n",
    "\n",
    "df_columns = ['data', 'cluster_id', 'channel', 'station']\n",
    "df = pd.DataFrame(data=[[data,1,channels,station_ids]],columns=df_columns)\n",
    "df.to_csv('test.csv',index=False)\n",
    "\n",
    "print(\"————————————————————————\")\n",
    "df_new = pd.read_csv('test.csv')\n",
    "data = df_new.at[0,'data']\n",
    "station = df_new.at[0,'channel']\n",
    "cluster = df_new.at[0,'cluster_id']\n",
    "channel = df_new.at[0,'channel']\n",
    "print(type(data))\n",
    "print(type(station))\n",
    "print(type(cluster))\n",
    "print(type(channel))\n",
    "print(\"————————————————————————\")\n",
    "data = json.loads(data)\n",
    "station = json.loads(station)\n",
    "# cluster = json.loads(cluster)\n",
    "channel = json.loads(channel)\n",
    "print(type(data))\n",
    "print(type(station))\n",
    "print(type(cluster))\n",
    "print(type(channel))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 2. 3. 4.]\n",
      " [2. 3. 4. 5.]\n",
      " [3. 4. 5. 6.]]\n",
      "[2.5 3.5 4.5]\n",
      "[1.118034 1.118034 1.118034]\n",
      "noise = [1.95139464 4.10220133 1.34686333 3.53456551]\n",
      "noise = [3.35882413 1.75740804 3.46506208 3.64905908]\n",
      "noise = [2.10160219 6.00247016 4.4717262  5.1432846 ]\n",
      "[[ 2.9513946  6.1022015  4.3468633  7.5345654]\n",
      " [ 5.3588243  4.757408   7.465062   8.649059 ]\n",
      " [ 5.101602  10.00247    9.471726  11.143285 ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "l = [[1,2,3,4],[2,3,4,5],[3,4,5,6]]\n",
    "data = np.array(l,dtype=np.float32)\n",
    "print(data)\n",
    "mean = np.mean(data,axis=1)\n",
    "print(mean)\n",
    "std = np.std(data,axis=1)\n",
    "print(std)\n",
    "for i in range(mean.shape[0]):\n",
    "    noise = np.random.normal(loc=mean[i],scale=std[i],size=data[0].shape[0])\n",
    "    print(\"noise = {}\".format(noise))\n",
    "    data[i,:]+=noise\n",
    "    # print(data[i,:]+1)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape=(3, 1000)\n",
      "mean shape=(3,)\n",
      "noise shape=(1000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noise shape=(1000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noise shape=(1000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def wgn(x, snr):\n",
    "    len_x = x.shape[0]\n",
    "    Ps = np.sum(np.power(x, 2)) / len_x\n",
    "    Pn = Ps / (np.power(10, snr / 10))\n",
    "    noise = np.random.randn(len_x) * np.sqrt(Pn)\n",
    "    return x + noise\n",
    "\n",
    "data = np.load('G:\\\\data\\\\mytrain\\\\positive\\\\GSOK027_4-2014.npy')\n",
    "data = data[0]\n",
    "print(\"data shape={}\".format(data.shape))\n",
    "mean = np.mean(data,axis=1)\n",
    "print(\"mean shape={}\".format(mean.shape))\n",
    "std = np.std(data,axis=1)\n",
    "for i in range(mean.shape[0]):\n",
    "    noise = np.random.normal(loc=mean[i],scale=1.2*std[i],size=data.shape[1])\n",
    "    # noise = wgn(data[i],50)\n",
    "    print(\"noise shape={}\".format(noise.shape))\n",
    "    plt.plot(data[i],c='g',label='data')\n",
    "    # plt.show()\n",
    "    data[i]+=noise\n",
    "    plt.plot(data[i],c='k',label='data')\n",
    "    # plt.plot(noise,c='g',label='noise')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year = 2014, month = 2\n",
      "year = 2014, month = 3\n",
      "year = 2014, month = 4\n",
      "year = 2014, month = 5\n",
      "year = 2014, month = 6\n",
      "year = 2014, month = 8\n",
      "year = 2014, month = 9\n",
      "year = 2014, month = 2\n",
      "year = 2014, month = 3\n",
      "year = 2014, month = 4\n",
      "year = 2014, month = 5\n",
      "year = 2014, month = 6\n",
      "year = 2014, month = 8\n",
      "year = 2014, month = 9\n"
     ]
    }
   ],
   "source": [
    "from obspy.core import read\n",
    "import os\n",
    "import pandas as pd\n",
    "from ConvNetQuake_pytorch.constant import CATALOG_BENZ_PATH, STREAM_PATH, DATA_ROOT_PATH\n",
    "from obspy.core.utcdatetime import UTCDateTime\n",
    "\n",
    "file_list = os.listdir('G:/data/streams')\n",
    "for file in file_list:\n",
    "    date = file.split('_')[-1]\n",
    "    date = date.split('.')[0]\n",
    "    month, year = date.split('-')\n",
    "    if year>'2014' or month<'2' or month >'9':\n",
    "        continue\n",
    "    print(\"year = {}, month = {}\".format(year,month))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "[False False  True  True  True  True False False]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "s = np.array([True,True,True,True,False,False,False,False])\n",
    "e = np.array([False,False,True,True,False,False,True,True])\n",
    "n = np.array([False,False,False,False,True,True,True,True])\n",
    "res = np.where(s==n)\n",
    "print(res[0].shape[0])\n",
    "print(s==e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2870, 3, 1000)\n",
      "(100, 3, 1000)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "# npy = np.load(r'G:\\data\\mytrain\\noise\\GSOK027_2-2014_0.npy')\n",
    "npy = np.load('test.npy')\n",
    "print(npy.shape)\n",
    "npy = npy[:100,...]\n",
    "print(npy.shape)\n",
    "np.save('test.npy',npy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_22668\\1300201273.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2.2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "for i in range(0,22):\n",
    "    print(i/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50109, 3, 1000)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "# data = np.load(r'F:\\gitee\\research_postgraduate\\earthquake_detection\\earthquake_detection_pytorch\\data\\mytrain\\train\\data_12.npy')\n",
    "data = np.load(r'G:\\data\\mytrain\\train\\data_13.npy')\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "task t1\n",
      "taskmain 0\n",
      " t2\n",
      "main 1\n",
      "2s\n",
      "main 2\n",
      "2s\n",
      "main 3\n",
      "main 4\n",
      "1s\n",
      "1s\n",
      "main 5\n",
      "main 6\n",
      "0s\n",
      "main 7\n",
      "0s\n",
      "main 8\n",
      "main 9\n"
     ]
    }
   ],
   "source": [
    "import threading\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "def run(n):\n",
    "    print(\"task\", n)\n",
    "    time.sleep(1)\n",
    "    print('2s')\n",
    "    time.sleep(1)\n",
    "    print('1s')\n",
    "    time.sleep(1)\n",
    "    print('0s')\n",
    "    time.sleep(1)\n",
    "data = []\n",
    "t1 = threading.Thread(target=run, args=(\"t1\",))\n",
    "t1.start()\n",
    "for i in range(10):\n",
    "    time.sleep(0.3)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "6deb3acb4775e1a6ef7712163535d77e1da27b51ccd00d7291c9cbc11bb8cdf3"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 ('pytorch-gpu')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
